Ai Edge Torch Documentation. export import export from torch_xla. We are excited to see wh

export import export from torch_xla. We are excited to see what the community builds with ExecuTorch’s on-device inference capabilities across mobile and edge devices backed by our industry partner delegates. Dec 22, 2025 · The AI Edge Torch Generative API is a high-performance library designed for authoring and converting transformer-based PyTorch models into the LiteRT/LiteRT-LM format. 0 97 30 248 Updated 17 hours ago ai-edge-torch Public Supporting PyTorch models with the Google AI Edge TFLite runtime. - google-ai-edge/ai-edge-torch PyTorch documentation # PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Who Is This For? May 14, 2024 · Under the hood, ai_edge_torch. x way to export PyTorch models into standardized model representations intended to be run on different environments. Sep 14, 2025 · The AI Edge Torch Generative API is a Torch native library for authoring mobile-optimized PyTorch Transformer models, which can be converted to TFLite, allowing users to easily deploy Large Language Models (LLMs) on mobile devices. The AI Edge Torch Generative API is a Torch native library for authoring mobile-optimized PyTorch Transformer models, which can be converted to TFLite, allowing users to easily deploy Large Language Models (LLMs) on mobile devices. PyTorch documentation # PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. SAM forms the heart of the Segment Anything initiative, a groundbreaking project that introduces a novel model, task, and dataset for image segmentation. from torch. base repository:google-ai-edge/ai-edge-torch Jan 9, 2026 · PyTorch Foundation is the deep learning community home for the open source PyTorch framework and ecosystem. Develop your applications with both generative and conventional AI models, coming from the most popular model frameworks. Built-in optimizations speed up training and inferencing with your existing technology stack. google-ai-edge / ai-edge-torch Public Notifications You must be signed in to change notification settings Fork 129 Star 879 May 14, 2024 · Under the hood, ai_edge_torch. 0 stable release. To run the tutorials below, make sure you have the torch and numpy packages installed. Sep 2, 2025 · Models converted with AI Edge Torch are compatible with the LLM Inference API and can run on the CPU backend, making them appropriate for Android and iOS applications. Qualcomm AI Engine Backend # In this tutorial we will walk you through the process of getting started to build ExecuTorch for Qualcomm AI Engine Direct and running a model on it. 6 days ago · The Segment Anything Model, or SAM, is a cutting-edge image segmentation model that allows for promptable segmentation, providing unparalleled versatility in image analysis tasks. stablehlo import exported_program_to_stablehlo import torch_xla. This enables developers to seamlessly deploy generative AI models, specifically Large Language Models (LLMs), for on-device text and image generation with ease. Discover the benefits of Azure OpenAI Instantly access cutting-edge foundational models and powerful reasoning models from OpenAI. Table of Contents Tensors Warm-up: numpy PyTorch: Tensors Autograd PyTorch: Tensors and autograd PyTorch: Defining new autograd functions nn module PyTorch: nn PyTorch: optim PyTorch: Custom nn Modules PyTorch: Control Flow + Weight Sharing Examples Tensors Autograd nn module Tensors # Warm-up: numpy OpenVINO is an open-source toolkit for deploying performant AI solutions in the cloud, on-prem, and on the edge alike. torchvision This library is part of the PyTorch project. Features Added ai_edge_torch. Compatible with torch 2. data. randn(4, 3, 224, 224), ) output = resnet18(*sample_input May 22, 2025 · google-ai-edge / ai-edge-torch Public Notifications You must be signed in to change notification settings Fork 135 Star 904 To run the tutorials below, make sure you have the torch and numpy packages installed. Cross-platform accelerated machine learning. pip install ai-edge-torch(-nightly) is now the only command needed to install ai-edge-torch and all dependencies. Lightning evolves with you as your projects go from idea to paper/production. If you need to, you can also compare across forks Learn more about diff comparisons here. Project description Library that supports converting PyTorch models into a . . Jan 5, 2026 · Microsoft Foundry is a trusted platform that empowers developers to drive innovation and shape the future with AI in a safe, secure, and responsible way. Table of Contents Tensors Warm-up: numpy PyTorch: Tensors Autograd PyTorch: Tensors and autograd PyTorch: Defining new autograd functions nn module PyTorch: nn PyTorch: optim PyTorch: Custom nn Modules PyTorch: Control Flow + Weight Sharing Examples Tensors Autograd nn module Tensors # Warm-up: numpy The AI Edge Torch Generative API is a Torch native library for authoring mobile-optimized PyTorch Transformer models, which can be converted to TFLite, allowing users to easily deploy Large Language Models (LLMs) on mobile devices. Google AI Edge Torch is a Python library that enables developers to convert PyTorch models into TensorFlow Lite (. The first post in the series introduced Google AI Edge Torch, which enables high performance inference of PyTorch models on mobile devices using the TFLite runtime. Developer documentation focuses on the OpenVINO architecture and describes building and contributing processes. Read the documentation from PreTrainedConfig for more information. TensorRT provides developers a unified path to deploy intelligent video analytics, speech AI, recommender systems, video conferencing, AI-based cybersecurity, and streaming apps in production. Jan 6, 2026 · Learn how to install, configure, and run your first AI model with Foundry Local Built to make you extraordinarily productive, Cursor is the best way to code with AI. Fast and accurate automatic speech recognition (ASR) for edge devices - moonshine-ai/moonshine Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP openai/clip-vit-base-patch32 architecture. xla_device() resnet18 = torchvision. tflite) format for on-device deployment. DataLoader and torch. 0 release of ai_edge_torch Jun 26, 2024 · Model Explorer, a new graph visualization tool from Google AI Edge, enables developers to overcome the complexities of optimizing models for edge devices. resnet18() # Sample input is a tuple sample_input = (torch. 0 release of ai_edge_torch AI Edge Torch Generative API System Architecture Overview This document aims to provide a technical deep dive of the AI Edge Torch Generative API, discuss its design considerations, system architecture and major components, current limitations, and future plans for improved system health, usability and performance. _search_model API Performance Improvements Improved layout optimization algorithm and general model Open a pull request Create a new pull request by comparing changes across two branches. 1. Dec 17, 2025 · Install steps and additional details are in the AI Edge Torch GitHub repository. PyTorch provides two data primitives: torch. nn. Features described in this documentation are classified by release status: Stable (API-Stable): These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. Serving models? Use LitServe to build custom inference servers in pure Python. Convert, optimize, and run inference utilizing the full potential of Intel® hardware. convert() is integrated with TorchDynamo using torch. Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Start using this task by following one of these implementation guides for your target platform. ExecuTorch: Deploy PyTorch models on mobile and edge devices with portability and performance. PyTorch is an open source machine learning framework. xla_model as xm import torchvision import torch xla_device = xm. utils. C++ 729 Apache-2. Supporting PyTorch models with the Google AI Edge TFLite runtime. core. models. Sep 20, 2025 · This guide provides step-by-step instructions for installing and using AI Edge Torch, a library that enables converting PyTorch models to TFLite format for deployment on edge devices including Android, iOS, and IoT devices. Learn more about PyTorch Edge and ExecuTorch. From there, you can deploy it using the standard LiteRT runtime. The conversion process also requires a model's sample input for tracing and shape inference. tflite format, which can then be run with TensorFlow Lite and MediaPipe. Lightning is the AI cloud for developers and AI teams that makes it easy to build and deploy lightning fast models. Our current implementation supports more than 60% of core_aten operators, which we plan to increase significantly as we build towards a 1. debug. For a comprehensive overview of the features, supported models, and configuration options, refer to the official Google AI Edge Torch documentation. We would like to show you a description here but the site won’t allow us. Sep 20, 2025 · This guide provides step-by-step instructions for installing and using AI Edge Torch, a library that enables converting PyTorch models to TFLite format for deployment on edge devices including Android Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch Supporting PyTorch models with the Google AI Edge TFLite runtime. Accelerate your AI innovation journey by rapidly deploying models optimized for complex problem-solving, logical reasoning, and multimodal capabilities including real-time audio. Documentation User documentation contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications. to_channel_last_io API (doc) Added ai_edge_torch. - google-ai-edge/ai-edge-torch The tools and frameworks that power Google's apps Explore the full AI edge stack, with products at every level — from low-code APIs down to hardware specific acceleration libraries. Our experts and AI copilots help at every step. This enables applications for Android, iOS and IOT that can run models completely on-device. parametrize to put constraints on your parameters (e. Install steps and additional details are in the AI Edge Torch GitHub repository. 4. export - which is the PyTorch 2. Users can convert the models using the AI Edge Torch PyTorch Converter, and run them via the TensorFlow Lite runtime. Note: The source PyTorch model needs to be compliant with torch. Oct 9, 2019 · Quantization API Reference (Kept since APIs are still public) # The Quantization API Reference contains documentation of quantization APIs, such as quantization passes, quantized tensor operations, and supported quantized modules and functions. export introduced in PyTorch 2. 0 . make them orthogonal, symmetric positive definite, low-rank) Model Optimization, Best Practice Jan 13, 2026 · Last, NVIDIA Triton Inference Server is open-source inference-serving software that enables teams to deploy trained AI models from any framework (TensorFlow, TensorRT, PyTorch, ONNX Runtime, or a custom framework), from local storage or Google Cloud Platform or AWS S3 on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Quick start • Examples • PyTorch Lightning • Fabric • Lightning Cloud • Community • Docs PyTorch Lightning is the deep learning framework for professional AI researchers and machine learning engineers who need maximal flexibility without sacrificing performance at scale. Features described in this documentation are classified by release status: Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. Released today, AI Edge Torch enables support for PyTorch, JAX, Keras, and TensorFlow with TFLite. The AI Edge Torch Generative API is a Torch native library for authoring mobile-optimized PyTorch Transformer models, which can be converted to TFLite, allowing users to easily deploy Large Language Models (LLMs) on mobile devices. The deep learning framework to pretrain and finetune AI models. Learn the Basics || Quickstart || Tensors || Datasets & DataLoaders || Transforms || Build Model || Autograd || Optimization || Save & Load Model Learn the Basics # Created On: Feb 09, 2021 | Last Updated: Dec 04, 2025 | Last Verified: Nov 05, 2024 Authors: Suraj Subramanian, Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein Most machine learning workflows involve working with data Oct 17, 2023 · PyTorch Edge is the future of the on-device AI stack and ecosystem for PyTorch. May 29, 2024 · AI Edge Torch Generative API enables developers to bring powerful new capabilities on-device, such as summarization, content generation, and more. PyTorch Lightning is the deep learning framework for professional AI researchers and machine learning engineers who need maximal flexibility without sacrificing performance at scale. g. We also expect to maintain backwards compatibility (although TensorFlow Lite, now named LiteRT, is still the same high-performance runtime for on-device AI, but with an expanded vision to support models authored in PyTorch, JAX, and Keras. Learn how to use torch. tflite format, and use AI Edge Quantizer to optimize the model for optimal performance under resource constraints. We also expect to maintain backwards compatibility (although The AI Edge Torch Generative API is a Torch native library for authoring mobile-optimized PyTorch Transformer models, which can be converted to TFLite, allowing users to easily deploy Large Language Models (LLMs) on mobile devices. The convert function provided by the ai_edge_torch package allows conversion from a PyTorch model to an on-device model. Jun 26, 2024 · Model Explorer, a new graph visualization tool from Google AI Edge, enables developers to overcome the complexities of optimizing models for edge devices. Goal: Convert a model from PyTorch to run on LiteRT. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples. AI Edge Torch Generative API Our Generative API library provides PyTorch native building blocks for composing Transformer models such as Gemma, TinyLlama and others using mobile-friendly abstractions, through which we can guarantee conversion, and performant execution on our mobile runtime, LiteRT. Path1 (classic models): Use the AI Edge Torch Converter to transform your PyTorch model into the . Qualcomm AI Engine Direct is also referred to as QNN in the source and documentation. Dataset that allow you to use pre-loaded datasets as well as your own data. May 29, 2024 · This is the second in a series of blog posts covering Google AI Edge developer releases.

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